R滞后/超前不规则时间序列数据 [英] R lag/lead irregular time series data

问题描述

我有不规则的时间序列数据帧，其中包含time(秒)和value列.我想添加另一列value_2，其中值以delay秒为单位.因此，时间t的value_2等于时间t + delay或之后的value.

I have irregular time series data frame with time (seconds) and value columns. I want to add another column, value_2 where values are lead by delay seconds. So value_2 at time t equals to value at time t + delay or right after that.

ts=data.frame(
  time=c(1,2,3,5,8,10,11,15,20,23),
  value=c(1,2,3,4,5,6,7,8,9,10)
)

ts_with_delayed_value <- add_delayed_value(ts, "value", 2, "time")

> ts_with_delayed_value
   time value value_2
1     1     1       3
2     2     2       4
3     3     3       4
4     5     4       5
5     8     5       6
6    10     6       8
7    11     7       8
8    15     8       9
9    20     9      10
10   23    10      10

我有此函数的自己的版本add_delayed_value，这里是:

I have my own version of this function add_delayed_value, here it is:

add_delayed_value <- function(data, colname, delay, colname_time) {
  colname_delayed <- paste(colname, sprintf("%d", delay), sep="_")
  data[colname_delayed] <- NaN

  for (i in 1:nrow(data)) {
    time_delayed <- data[i, colname_time] + delay
    value_delayed <- data[data[colname_time] >= time_delayed, colname][1]
    if (is.na(value_delayed)) {
      value_delayed <- data[i, colname]
    }
    data[i, colname_delayed] <- value_delayed
  }

  return(data)
}

有没有一种方法可以向量化此例程以避免慢循环?

Is there a way to vectorize this routine to avoid the slow loop?

我对R很陌生，所以这段代码可能有很多问题.有什么可以改进的吗?

I'm quite new to R, so this code probably has lots of issues. What can be improved about it?

推荐答案

您可以尝试:

library(dplyr)
library(zoo)
na.locf(ts$value[sapply(ts$time, function(x) min(which(ts$time - x >=2 )))])
[1]  3  4  4  5  6  8  8  9 10 10

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